System and method for conscious machines

ABSTRACT

Consciousness is widely considered to be a mysterious and uniquely human trait, which cannot be achieved artificially. On the contrary, a system and method are disclosed for a computational machine that can recognize itself and other agents in a dynamic environment, in a way that seems quite similar to biological consciousness in humans and animals. The machine comprises an artificial neural network configured to identify correlated temporal patterns and attribute causality and agency. The machine is further configured to construct a virtual reality environment of agents and objects based on sensor inputs, to create a coherent narrative, and to select future actions to pursue goals. Such a machine may have application to enhanced decision-making in autonomous vehicles, robotic agents, and intelligent digital assistants.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation of U.S. patent applicationSer. No. 16/281,956, filed Feb. 21, 2019, now U.S. Pat. No. 11,119,483,issued Sep. 14, 2021, which is a non-provisional of, and claims benefitof priority under 35 U.S.C. § 119(e) from, U.S. Provisional PatentApplication No. 62/633,950, filed Feb. 22, 2018, the entirety of whichis expressly incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to the field of artificial intelligence,and more particularly to self-aware automatons.

BACKGROUND OF THE INVENTION

Since the beginning of computer technology in the mid-20^(th) century,computers have commonly been characterized as “electronic brains”, butthis is based on several misunderstandings. First, computers, going backto their origins by Turing and Von Neumann, were designed to carry outarithmetic and logic operations, as in the classic Von Neumann computerarchitecture of FIG. 1 (see, for example,en.wikipedia.org/wikiNon_Neumann_architecture). In contrast, biologicalbrains have evolved to match patterns, and are organized quitedifferently on both the “device” and “architectural” levels. Thisdistinction has been recognized in the prior art, and led to thedevelopment of computational systems known as “artificial neuralnetworks” (also known as “Neural Networks”, see, for example,en.wikipedia.org/wiki/Artificial_neural_network), comprising aninterconnected network of artificial neurons, as shown in FIG. 2,connected via an array of artificial synapses. See:

-   Schmidhuber, Jürgen. “Deep learning in neural networks: An    overview.” Neural networks 61 (2015): 85-117.-   Grossberg, Stephen. “Nonlinear neural networks: Principles,    mechanisms, and architectures.” Neural networks 1, no. 1 (1988):    17-61.-   Kosko, Bart. Stability and Adaptation of Neural Networks. University    Of Southern California Los Angeles Signal And Image Processing Inst,    1990.-   Bishop, Christopher M. Neural networks for pattern recognition.    Oxford university press, 1995.-   Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.    Learning internal representations by error propagation. No.    ICS-8506. California Univ San Diego La Jolla Inst for Cognitive    Science, 1985.-   Vapnik, Vladimir. The nature of statistical learning theory.    Springer science & business media, 2013.-   Kohonen, Teuvo. “The self-organizing map.” Neurocomputing 21, no.    1-3 (1998): 1-6.-   Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern    classification. John Wiley & Sons, 2012.-   Vapnik, Vladimir Naumovich. “An overview of statistical learning    theory.” IEEE transactions on neural networks 10, no. 5 (1999):    988-999.-   Hagan, Martin T., and Mohammad B. Menhaj. “Training feedforward    networks with the Marquardt algorithm.” IEEE transactions on Neural    Networks 5, no. 6 (1994): 989-993.-   Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.    “Learning representations by back-propagating errors.” nature 323,    no. 6088 (1986): 533.-   Rumelhart, David E., James L. McClelland, and PDP Research Group.    Parallel distributed processing. Vol. 1. Cambridge, Mass.: MIT    press, 1987.-   Hopfield, John J. “Neural networks and physical systems with    emergent collective computational abilities.” Proceedings of the    national academy of sciences 79, no. 8 (1982): 2554-2558.-   Hertz, John, Anders Krogh, and Richard G. Palmer. Introduction to    the theory of neural computation. Addison-Wesley/Addison Wesley    Longman, 1991.-   Cortes, Corinna, and Vladimir Vapnik. “Support-vector networks.”    Machine learning 20, no. 3 (1995): 273-297.-   Narendra, Kumpati S., and Kannan Parthasarathy. “Identification and    control of dynamical systems using neural networks.” IEEE    Transactions on neural networks 1, no. 1 (1990): 4-27.-   Hebb, Donald Olding. The organization of behavior: A    neuropsychological theory. Psychology Press, 2005.-   Broomhead, David S., and David Lowe. Radial basis functions,    multi-variable functional interpolation and adaptive networks. No.    RSRE-MEMO-4148. Royal Signals and Radar Establishment Malvern    (United Kingdom), 1988.-   Rosenblatt, F. “The Perceptron: A Probabilistic Model For    Information Storage And Organization In The Brain.” Psychological    Review 65, no. 6: 1958.-   Burges, Christopher JC. “A tutorial on support vector machines for    pattern recognition.” Data mining and knowledge discovery 2, no. 2    (1998): 121-167.-   Poggio, Tomaso, and Federico Girosi. “Networks for approximation and    learning.” Proceedings of the IEEE 78, no. 9 (1990): 1481-1497.

Neural networks comprise massive parallelism with distributed computingand memory, in contrast to the centralized arithmetic/logic unit andseparate memory of the classic von Neumann computer. Neural networksprovide an example of brain-inspired or “neuromorphic” computing, whichemulates one or more aspects of biological information processing.Biological neurons conduct voltage spikes, but this aspect is notessential for neural networks. Several variants of neural networks havebeen recognized in the prior art, including recurrent and convolutionalneural networks, reflecting different degrees of feedback andfeedforward circuits. Note that von Neumann computers can simulateneural networks, but not with the efficiency and speed of neural networkhardware.

Brains in biological systems are based on large arrays of neurons, whereeach neuron may have thousands of synapses connecting it to otherneurons. For example, the cerebral cortex of a mouse may have about 4million neurons, and that of a human may have 16 billion neurons. See,for example, en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons.In comparison, the neuromorphic chip from IBM known as True Northcomprises one million artificial neurons, each with 256 synapses; seeen.wikipedia.org/wiki/TrueNorth.

A related aspect of the prior art has been the field of “artificialintelligence” (AI), which has focused on emulating some of thefunctionality of biological intelligent systems (see for exampleen.wikipedia.org/wiki/Artificial_intelligence). Early AI systemscomprised a body of knowledge in a particular topic, and a set of ruleson how to apply this knowledge. This may be analogous to biologicalsystems that operate on instinct, where the rules are pre-programmed andrigid, and the biological system is not generally viewed as particularlyintelligent. Such an AI system may be implemented in software based onconventional computer hardware. More recent AI systems have comprisedneural networks with multiple “hidden layers” between input and output,which are capable of learning or training; the term “deep learning” hasbeen applied to some of these systems (see, for example,en.wikipedia.org/wiki/Deep_learning). The training may comprise bothsupervised and unsupervised learning. Some deep learning systems havebeen very effective at showing human-like knowledge in games and inexpert systems, such as IBM Watson (seeen.wikipedia.org/wiki/Watson_(computer)).

One biological aspect that has not significantly been implemented inprior-art computing technology is biological consciousness. See, forexample, en.wikipedia.org/wiki/Artificial_consciousness; also non-patentliterature R. Manzotti 2013, “Machine Consciousness: A Modem Approach”,Natural Intelligence: The INNS Mag., vol. 2, pp. 7-18; J. A. Reggia2013, “The Rise of Machine Consciousness: Studying Consciousness withComputational Models”, Neural Networks 44, pp. 112-131. Robots withhuman-like consciousness have of course been a mainstay of sciencefiction, but there have been few practical disclosures or demonstrationsof how this property may be synthesized artificially, and these have notbeen clear and complete. Much of the difficulty is that the property ofconsciousness in biological systems is not well defined or understood,so it is unclear how to address this. Consciousness is generally viewedas more of a question in philosophy or religion, rather than science ortechnology. This is because the key aspect of consciousness that isperceptible to humans is the recognition of self, separate from the restof the world. This perception forms the basis for Cartesian dualism,also known as mind-body dualism, which argues that the mind is of adifferent nature than the physical world.

Ever since digital computers were first developed in the 1950s, theywere commonly thought of as “electronic brains”. But traditionalcomputers are actually quite different from brains, both in structureand capabilities. However, recent research in alternative computerarchitectures, combined with research on the brain, has shown that“neuromorphic” computer architectures may finally be emulating brainsmore closely.

This difference can be seen in FIG. 1 and FIG. 2 of the prior art, whichcompare a traditional von Neumann computer architecture to a neuralnetwork. The von Neumann architecture in FIG. 1 extended a simplemathematical model of a computer proposed by Alan Turing, and wasbrought to practical fruition under the direction of John von Neumann atthe Institute for Advanced Study in Princeton in the 1950s. Thisarchitecture consists basically of an arithmetic engine with a controlprogram and memory. Virtually all practical computers since then havehad the same basic structure. Even modern supercomputers with multipleprocessors and memory hierarchies are based on this original design.

Compare this to a basic design of an artificial neural network (orneural net) shown in FIG. 2. This represents an array of artificialneurons (which may be electronic elements or circuits) organized intolayers and connected with “synapses”. The strength of a given synapsemay be changed according to a procedure of iterative training orlearning, rather than an imposed program. The collection of synapsestrengths correspond to memory, but are distributed throughout thesystem, rather than localized in a single module. Furthermore, there isno central processing unit; the processing is also distributed. Thisstructure is similar in several respects to the interconnections betweenbiological neurons, although it is much simpler.

The von Neumann architecture represents a universal computer, so that itcan be used to simulate any other computer architecture, including aneural net. But such a simulation would be very inefficient and slow. Infact, a traditional processor works very well in doing mathematicalcalculations, whereas brains do mathematics rather poorly. On the otherhand, brains have evolved to match patterns, and do this very well.Pattern matching is not limited to image recognition, but occurs in allsensory processing, memory retrieval, language translation, and a hostof correlation and optimization problems. Recent research has shown thatneural nets with many “hidden layers” between the input and output canbe trained to be particularly efficient in learning to match patterns;this is known as “deep learning”. More hidden layers enable moreabstract correlations among inputs, which in turn enables more flexiblerecognition of a wider variety of complex patterns. The learning processitself is adaptive in a way that is similar to evolution. Theenvironment selects those variations that are most effective in matchingthe training patterns.

Another important aspect of brains is that they are composed of basicelements (the neurons) that are slow, noisy, and unreliable. But despitethis, brains have evolved to respond quickly in complex environments, bytaking advantage of distributed computing with massive parallelism, andneural nets are uniquely capable to taking full advantage of this. It isremarkable that transistors, the basic elements of modem computers, arenow a million times faster than biological neurons (characteristic timesof nanoseconds rather than milliseconds), but brains are still farfaster and more energy-efficient than traditional supercomputers in thetypes of pattern matching tasks for which brains have evolved. Acomputer with a brain-like organization but electronic speed wouldrepresent a major technological breakthrough.

The field of artificial intelligence is almost as old as computersthemselves, but it has long fallen far short of its goals. Thetraditional method of artificial intelligence is to devise a list ofrules about a particular topic, and program them into a conventionalcomputer. But knowledge of a fixed set of rigid rules is not what wegenerally mean by intelligence. Indeed, biological organisms with verysimple nervous systems, such as worms or insects, behave as if they are“hard-wired” with rule-driven behavior, and are not regarded asintelligent at all. Furthermore, sophisticated responses to dynamicenvironments cannot be fully programmed in advance; there must be astrong element of learning involved. In contrast, neural nets can learnhow to recognize a cat, for example, without being told explicitly whatdefines a cat. The newer “deep learning” approach to artificialintelligence is starting to have a major impact on technology. Intraditional computers, the greatest difficulty is found in writing anddebugging the software. In contrast, in both natural intelligence andthe newer approaches to machine learning, the software is generatedautomatically via learning. No programmer is necessary; as withevolution, this is unguided.

There are several patents and patent applications that propose toaddress artificial consciousness. There is no evidence that any of thesehave been reduced to practice, in whole or in part.

The set of U.S. Pat. Nos. 7,499,893, 7,849,026, 8,065,245, and8,346,699, invented by Gregory Czora, proposes a “System and Method forSimulating Consciousness.” Czora discloses a Digital Life Form thatfunctions as a human-computer interface, which interacts with objects inan environment in a way that simulates human consciousness. It is notclear in these patents what consciousness is, or how it is to beimplemented, except that it may “emerge from the interaction of theDigital Life Form with its environment and its own previous actions.”

The published US patent application 2017/0236054, invented by JohnCarson, discloses a “Hyper Aware Logic to Create an Agent ofConsciousness and Intent for Devices and Machines”. Carson proposes thata set of neural logic units could create an agent that is aware ofitself and its environment, and can make decisions on its own. However,Carson does not disclose an architecture or a mechanism that can createsuch self-awareness, beyond simply connecting sensor inputs to theneural logic units.

US patent application 2008/0256008, invented by Mitchell Kwok, disclosesa “Human Artificial Intelligence Machine”. This describes aself-learning, self-aware system that can identify objects in theenvironment and project events to the future. However, no mechanism forself-awareness is described.

US patent application 2012/0059781, invented by Nam Kim, discloses“Systems and Methods for Creating or Simulating Self-Awareness in aMachine”. This describes a conscious system with an artificialpersonality, receiving information from sensors, but apart from afeedback signal, no basis for consciousness is given.

US patent application 2014/0046891, invented by Sarah Banas, discloses a“Sapient or Sentient Artificial Intelligence”. Banas describes a systemwith natural language input and output that uses a neural network toprovide human-like responses, and asserts that this system may beconscious or self-aware, but does not describe a mechanism for thisself-awareness.

All of this prior art is missing a key element in artificialconsciousness: temporal pattern recognition. Pattern or imagerecognition of objects in space is well known (seehttps://en.wikipedia.org/wiki/Pattern_recognition), and this can beextended to evolution of events in time as well.

Temporal pattern recognition has been discussed in the prior art. See,for example, the following US patents:

U.S. Pat. No. 8,583,586, “Mining temporal patterns in longitudinal eventdata using discrete event matrices and sparse coding”, invented by S.Ebodallahi et al.

U.S. Pat. No. 8,346,692, “Spatio-temporal pattern recognition using aspiking neural network and processing thereof on a portable and/ordistributed computer”, invented by J. Rouat, et al.

U.S. Pat. No. 8,457,409, “Cortex-like learning machine for temporal andhierarchical pattern recognition”, invented by J. T. Lo.

U.S. Pat. No. 9,177,259, “Systems and methods for recognizing andreacting to spatiotemporal patterns”, invented by G. Levchuk.

U.S. Pat. No. 7,624,085, “Hierarchical based system for identifyingobject using spatial and temporal patterns,” invented by J. Hawkins andD. George.

U.S. Pat. No. 8,825,565, “Assessing performance in a spatial andtemporal memory system”, invented by R. Marianetti et al.

Another prior art technology that may be relevant to artificialconsciousness in the present application is virtual reality, whereby acomputer creates a simulated environment through which an observer canmove. See, for example, en.wikipedia.org/wikiNirtual_reality. Such avirtual reality environment may comprise elements derived from one ormore real environments, as well as simulations from models. Some priorart of this type may be known as augmented reality, mixed reality, orhybrid reality. There are many patents in this field, for example thefollowing US patents:

U.S. Pat. No. 9,251,721, “Interactive mixed reality systems and usesthereof,” invented by S. Lampotang et al.

U.S. Pat. No. 9,677,840, “Augmented reality simulator,” invented by S.Rublowsky.

U.S. Pat. No. 9,318,032, Hybrid physical-virtual reality simulation forclinical training, invented by J. Samosky.

U.S. Pat. No. 9,407,904, Method of obtaining 3D virtual reality from 2Dimages, invented by J. Sandrew et al.

Finally, there are different concepts of what capabilities would definea conscious machine, and how its consciousness could be recognized. Inone variant, the machine would be able to converse in natural languagewith a person (perhaps remotely), who could determine by questions andanswers whether this was really a machine. This is the basis for theclassic Turing test of machine intelligence (seeen.wikipedia.org/wiki/Turing_test), where a machine is consideredintelligent if it can fool a human into thinking it is another human.This may not be definitive, since humans are predisposed to see humanagency in others, and can easily be fooled. In the present application,a more general class of consciousness is considered, which might beanalogous to that in a trainable animal. Such a machine need not havefull language capabilities, but might exhibit flexibility and rapiddecision-making in a variety of environments.

What is needed is a computing machine that is designed to implement aform of biological consciousness, comprising recognition of self in avirtual environment.

See, U.S. Pat. Nos. 5,602,964; 5,943,663; 6,016,447; 6,016,448;6,154,675; 7,433,482; 7,570,991; 7,627,538; 7,765,171; 7,835,858;7,877,347; 7,899,760; 7,904,396; 7,925,600; 8,014,937; 8,041,655;8,099,181; 8,112,373; 8,135,655; 8,138,770; 8,140,452; 8,140,453;8,160,978; 8,165,916; 8,165,976; 8,165,977; 8,175,896; 8,229,221;8,239,336; 8,260,733; 8,271,411; 8,275,725; 8,290,768; 8,352,400;8,364,136; 8,369,967; 8,380,530; 8,386,378; 8,402,490; 8,426,531;8,452,719; 8,458,082; 8,468,244; 8,516,266; 8,521,488; 8,548,231;8,554,468; 8,554,707; 8,566,263; 8,566,264; 8,572,012; 8,583,263;8,583,574; 8,595,165; 8,600,926; 8,639,629; 8,645,292; 8,645,312;8,650,149; 8,655,829; 8,674,706; 8,676,742; 8,682,812; 8,694,442;8,694,449; 8,694,457; 8,719,213; 8,738,562; 8,756,077; 8,766,982;8,768,838; 8,781,982; 8,811,532; 8,818,917; 8,818,931; 8,843,433;8,862,527; 8,892,495; 8,928,232; 8,929,612; 8,930,178; 8,930,268;8,934,375; 8,934,445; 8,934,965; 8,941,512; 8,942,436; 8,942,466;8,945,829; 8,949,082; 8,949,287; 8,955,383; 8,958,386; 8,958,605;8,959,039; 8,964,719; 8,965,677; 8,965,824; 8,971,587; 8,972,316;8,976,269; 8,977,582; 8,977,629; 8,983,216; 8,983,882; 8,983,883;8,983,884; 8,989,515; 8,990,133; 9,002,682; 9,002,762; 9,002,776;9,007,197; 9,008,840; 9,009,088; 9,009,089; 9,014,416; 9,015,092;9,015,093; 9,017,656; 9,024,906; 9,031,844; 9,032,537; 9,037,464;9,037,519; 9,047,561; 9,047,568; 9,052,896; 9,053,431; 9,058,515;9,058,580; 9,060,392; 9,063,953; 9,064,161; 9,069,730; 9,070,039;9,078,629; 9,082,079; 9,082,083; 9,091,613; 9,092,692; 9,092,737;9,092,738; 9,098,811; 9,098,813; 9,101,279; 9,102,981; 9,103,671;9,103,837; 9,104,186; 9,104,973; 9,106,691; 9,111,215; 9,111,226;9,115,122; 9,117,133; 9,117,176; 9,122,956; 9,122,994; 9,123,127;9,127,313; 9,128,203; 9,129,167; 9,129,190; 9,129,221; 9,135,241;9,137,417; 9,141,906; 9,141,926; 9,146,546; 9,147,144; 9,147,154;9,147,156; 9,152,853; 9,152,881; 9,152,888; 9,152,915; 9,153,024;9,155,487; 9,156,165; 9,157,308; 9,158,967; 9,159,021; 9,159,027;9,159,584; 9,165,187; 9,165,188; 9,165,245; 9,165,259; 9,171,202;9,171,204; 9,171,247; 9,171,262; 9,171,263; 9,173,614; 9,175,352;9,177,476; 9,177,550; 9,180,309; 9,183,493; 9,189,472; 9,189,730;9,189,745; 9,189,749; 9,190,053; 9,191,138; 9,193,075; 9,194,803;9,195,865; 9,195,934; 9,195,949; 9,202,178; 9,202,203; 9,202,462;9,203,553; 9,203,911; 9,203,912; 9,208,431; 9,208,443; 9,208,536;9,209,782; 9,210,708; 9,211,314; 9,213,885; 9,213,937; 9,217,775;9,218,563; 9,218,573; 9,219,572; 9,220,634; 9,224,035; 9,224,068;9,224,090; 9,226,676; 9,229,910; 9,235,799; 9,235,887; 9,239,951;9,239,985; 9,251,420; 9,251,437; 9,253,349; 9,256,215; 9,256,322;9,256,823; 9,262,724; 9,263,060; 9,268,058; 9,268,990; 9,269,040;9,275,308; 9,275,326; 9,286,524; 9,290,010; 9,292,012; 9,298,172;9,302,103; 9,302,179; 9,307,944; 9,308,445; 9,311,298; 9,311,531;9,311,593; 9,311,594; 9,311,595; 9,311,596; 9,311,670; 9,311,915;9,317,727; 9,317,728; 9,317,740; 9,324,022; 9,324,154; 9,324,321;9,330,119; 9,333,415; 9,336,239; 9,336,480; 9,336,482; 9,336,781;9,342,742; 9,342,781; 9,344,211; 9,351,378; 9,355,312; 9,366,451;9,367,490; 9,367,602; 9,367,798; 9,367,799; 9,370,316; 9,373,038;9,373,057; 9,373,059; 9,373,324; 9,378,464; 9,378,731; 9,378,733;9,384,334; 9,384,335; 9,389,260; 9,390,371; 9,390,373; 9,390,712;9,391,789; 9,396,374; 9,396,388; 9,396,523; 9,400,589; 9,401,148;9,405,751; 9,405,975; 9,406,017; 9,412,009; 9,412,041; 9,412,064;9,412,065; 9,417,700; 9,418,334; 9,418,390; 9,423,403; 9,424,489;9,424,512; 9,428,767; 9,430,667; 9,430,817; 9,434,937; 9,436,909;9,436,913; 9,449,225; 9,449,271; 9,449,336; 9,451,899; 9,452,346;9,454,729; 9,454,730; 9,454,958; 9,456,131; 9,460,387; 9,460,400;9,460,557; 9,460,711; 9,461,535; 9,471,869; 9,472,187; 9,477,625;9,477,654; 9,477,655; 9,477,901; 9,477,906; 9,477,925; 9,480,402;9,482,672; 9,483,794; 9,484,015; 9,484,016; 9,489,621; 9,489,623;9,494,602; 9,495,395; 9,495,414; 9,507,983; 9,508,026; 9,508,164;9,508,340; 9,511,274; 9,514,357; 9,514,389; 9,514,405; 9,514,753;9,519,859; 9,520,127; 9,524,462; 9,525,699; 9,528,834; 9,529,794;9,530,047; 9,530,091; 9,532,762; 9,533,113; 9,534,234; 9,535,563;9,535,960; 9,542,553; 9,542,626; 9,547,804; 9,552,544; 9,552,546;9,552,547; 9,552,551; 9,558,742; 9,563,840; 9,569,650; 9,570,069;9,573,277; 9,574,209; 9,575,070; 9,576,214; 9,580,697; 9,589,565;9,590,425; 9,591,580; 9,594,983; 9,595,002; 9,598,734; 9,607,355;9,607,616; 9,609,436; 9,609,904; 9,612,248; 9,613,292; 9,613,297;9,613,466; 9,613,619; 9,614,724; 9,619,749; 9,619,881; 9,619,883;9,620,145; 9,621,681; 9,623,905; 9,626,566; 9,630,005; 9,630,011;9,630,348; 9,631,936; 9,631,943; 9,638,678; 9,640,186; 9,644,847;9,646,244; 9,646,634; 9,651,519; 9,652,712; 9,655,564; 9,663,831;9,665,100; 9,665,822; 9,665,823; 9,668,075; 9,671,953; 9,672,609;9,677,109; 9,678,059; 9,679,108; 9,679,258; 9,681,250; 9,681,815;9,687,208; 9,687,377; 9,689,826; 9,690,293; 9,690,776; 9,691,020;9,692,662; 9,696,719; 9,697,200; 9,697,826; 9,697,833; 9,700,785;9,703,929; 9,704,068; 9,705,998; 9,707,282; 9,709,986; 9,710,606;9,710,852; 9,713,982; 9,714,420; 9,720,907; 9,721,202; 9,721,214;9,721,338; 9,721,384; 9,721,561; 9,721,562; 9,725,769; 9,727,042;9,728,184; 9,730,098; 9,730,660; 9,732,385; 9,734,292; 9,734,824;9,735,833; 9,740,680; 9,740,782; 9,747,543; 9,753,796; 9,754,080;9,754,163; 9,754,371; 9,754,584; 9,757,561; 9,758,839; 9,760,090;9,760,676; 9,767,385; 9,767,410; 9,778,021; 9,778,807; 9,779,204;9,779,727; 9,779,786; 9,782,585; 9,784,748; 9,786,270; 9,788,179;9,789,313; 9,792,492; 9,792,501; 9,792,531; 9,796,479; 9,798,922;9,799,098; 9,799,327; 9,801,066; 9,802,042; 9,805,399; 9,805,716;9,811,718; 9,811,775; 9,817,399; 9,818,136; 9,818,239; 9,822,389;9,824,060; 9,824,294; 9,824,684; 9,826,351; 9,830,315; 9,833,184;9,836,455; 9,836,671; 9,836,883; 9,839,552; 9,842,106; 9,842,302;9,842,585; 9,842,610; 9,846,836; 9,847,974; 9,848,112; 9,848,827;9,849,044; 9,853,951; 9,858,263; 9,858,340; 9,858,496; 9,858,529;9,864,953; 9,870,589; 9,870,617; 9,870,629; 9,870,630; 9,874,914;9,875,440; 9,875,567; 9,875,737; 9,881,349; 9,881,613; 9,881,615;9,886,949; 9,891,716; 9,892,420; 9,895,077; 9,900,722; RE45768; RE45770;RE46310; 20010028339; 20010034478; 20010049585; 20020013664;20020032670; 20020066024; 20020069043; 20020078368; 20020091655;20020099675; 20020102526; 20020151992; 20020172403; 20030007674;20030023386; 20030055516; 20030055610; 20030065633; 20030089218;20030158768; 20030187587; 20030190603; 20030191728; 20030194124;20030212645; 20030225715; 20030235816; 20040015894; 20040019283;20040019469; 20040042662; 20040056174; 20040072162; 20040073376;20040076984; 20040086162; 20040101181; 20040121487; 20040127778;20040133533; 20040143725; 20040148265; 20040181497; 20040193559;20040213448; 20040220891; 20040233233; 20050004883; 20050019798;20050058242; 20050080462; 20050086186; 20050090991; 20050100208;20050104603; 20050108200; 20050117700; 20050169516; 20050213802;20050240085; 20050267011; 20050267688; 20050276485; 20050282146;20050282199; 20060017578; 20060018524; 20060024679; 20060031764;20060056704; 20060067573; 20060094001; 20060098876; 20060155398;20060195266; 20060200253; 20060200258; 20060200259; 20060200260;20060262726; 20070016476; 20070022062; 20070038588; 20070043452;20070053513; 20070061022; 20070061023; 20070061735; 20070070038;20070094166; 20070111269; 20070134725; 20070168227; 20070185825;20070239635; 20070239644; 20070244375; 20080014646; 20080033658;20080040749; 20080095429; 20080152217; 20080267999; 20080270120;20080281767; 20090018940; 20090041187; 20090043547; 20090104602;20090132449; 20090206234; 20090271344; 20090307165; 20100049339;20100067786; 20100070098; 20100076642; 20100077840; 20100085066;20100094788; 20100161530; 20100185573; 20100189342; 20100217145;20100228694; 20100278420; 20100316283; 20110029922; 20110033122;20110047110; 20110055131; 20110156896; 20110167110; 20110257950;20110276344; 20110288890; 20120017232; 20120036016; 20120041158;20120041915; 20120150651; 20120213331; 20130018833; 20130147598;20130243122; 20130342681; 20140000347; 20140005958; 20140058735;20140089241; 20140173452; 20140333326; 20140364721; 20150046354;20150081280; 20150204559; 20150204725; 20150269439; 20150377667;20160034814; 20160035093; 20160098629; 20160196480; 20160282351;20160300127; 20160350834; 20170011279; 20170011280; 20170024877;20170039186; 20170073737; 20170140262; 20170154258; 20170154259;20170161590; 20170169331; 20170169357; 20170193298; 20170286810;20170337587; 20170363475; and 20180018553.

All patent and non-patent references cited herein are expresslyincorporated herein by reference in their entirety.

SUMMARY OF THE INVENTION

We are all conscious, which might seem to make us all experts inconsciousness. However most of our internal observations aboutconsciousness are illusions, as shown by psychological research. Webelieve that there is a unified conscious mind which controls everythingwe do. However, most of our actions are actually controlledsubconsciously by components of an unconscious mind, even if theconscious mind takes credit for the actions. This is represented in FIG.3, which shows a conscious mind (C) above an unconscious mind (U), whichin turn is above an external environment (E). A good analogy is with theChairman of the Board (C) of a corporation, who believes that he runsthe corporation single-handedly, and is encouraged in this belief by hisunderlings (U). In fact, all routine actions are carried out by U,independently of C. Only those non-routine actions which require adecision are brought to the attention of C. Even then, U prepares asimplified model of the alternatives and their future implications to C,generally as a binary decision. This simplified model is designed toinsure a rapid decision, and may have embedded assumptions that arehidden from C. C believes that it is making an unbiased decision basedon all the evidence, but this, too, is an illusion. A machine emulationof consciousness may exhibit a similar structure.

According to the present application, consciousness represents thefunctioning of a specific architecture of a neural network that featuresself-recognition as a primary aspect.

In a preferred embodiment of the invention, a conscious machinesimilarly comprises a consciousness computing module, together with anunconscious computing module, as shown in the block diagram of FIG. 4.The consciousness computing module constitutes a dynamic virtual realityenvironment, comprising the self, objects, and other agents. Theunconscious module comprises a computational engine that accepts inputfrom an external physical environment and generates control outputs tothe external environment, as well as generating the virtual realityenvironment. The internal sense of consciousness is associated with theactivation of the virtual reality environment, which provides a coherentnarrative of the self, interacting with other components of the virtualreality environment. The virtual reality environment comprises arepresentation of the external environment, but they are not the same.

The virtual reality environment thus represents a subjective model ofthe external physical environment, and which differs from the externalphysical environment based on limited information, abstraction ofpatterns and features, biases, etc.

The input information to generate the virtual reality environment maycomprise visual images, auditory signals, local contact sensorinformation (“sense of touch”), and electronic information signals fromthe external reality. Of course, the inputs are not limited, and maycomprise any type of sensor, local or remote database, or borrowedvirtual reality environments of other systems.

The virtual reality environment is comprised of a plurality of objectsthat may interact, rather than merely images and signals. In effect, theexternal reality environment is deconstructed or partitioned intoobjects, and the objects are combined to reconstruct the virtual realityenvironment. An object represents an element in the external environmentthat maintains integrity in space and time, and is linked to memory ofsimilar elements in previous environments (external and virtual). Theclass of similar objects may be recognized by common features orattributes, such as size, shape, solidity, deformability, hardness,coldness, color, etc.

Typically, the virtual reality environment will conform to the laws ofphysics, though this is a requirement only for physically-accuratemodels. In some cases, deviations from the laws of physics in thevirtual reality environment may be employed.

While some objects are passive, a special type of object is active,i.e., is an agent. An agent can alter the environment, moving itself orother objects. For example, consider FIG. 5, which represents thelocation of an object and an agent in an environment. This shows aninitially stationary object, together with an agent that moves to theposition of the object, followed by motion of the object. The neuralnetwork in the unconscious module is configured to use pattern matchingto recognize that the motion of the agent is correlated with the motionof the object. In natural environments, such correlation is generallydue to causality, and the unconscious module further generates a modelof the agent moving continuously in the virtual environment, causing theobject to move. Such a model not only describes past actions in theenvironment, it also enables predictions of future actions byextrapolation.

The virtual reality environment operates according to predictable rulesand properties, and therefore may be used to explore cause-and-effectprior to taking action in the real world. This can be especially usefulwhere the real world includes another independent agent, or a task is orseems to be intractable or NP hard. In these cases, the machine mayexplore possible actions in the virtual reality environment, includingmulti-step actions, to predict outcomes in the real world. Theexploration may consider both outcome per se, and risks and margins oferror/safety.

A special type of agent is the self, the activity of which is correlatedwith control outputs and local sensor inputs. The self comprises theimage of the self in the virtual environment, together with filteredversions of these control outputs and local sensor inputs. Recognitionof the self comprises matching the present self with that in recent andearlier memories of the self. Other agents are distinguished by NOTbeing correlated with control outputs and local sensor inputs, e.g.,they act independently. The unconscious module further generates a modelof the self in the virtual environment, capable of accounting for pastactions and future predictions (see FIG. 6). The repeated activation ofthis model of the self as an agent in the virtual environment isdirectly analogous to what we ourselves experience as a sense ofconsciousness. This model of the self may also comprise a set of goalsand a simplified narrative. It may also include a set of positive andnegative associations (based on pre-programming or learned from priorexperiences), that may be analogous to emotions in biologicalconsciousness.

It is suggested that biological consciousness defined in this way is notuniquely human, but is widespread in the animal kingdom. Humanconsciousness may have additional properties of abstract language andabstract logical processing, but may otherwise be similar to that inanimals. Machine consciousness as described herein may be useful evenwith very limited conscious-level language and logical processing,perhaps analogous to that in a horse, for example.

When a decision between alternative future actions is necessary, two ormore future predictions may be projected within the model, and thefutures evaluated according to a predetermined (or learned) set ofgoals. This may provide an example of a conscious-level decision, butother decisions may be made sub-consciously, and justified after thefact. The perception of conscious-level unified control may be areflection of operation of self-recognition circuits, rather than actualcontrol by the model of the conscious self.

A key aspect of temporal pattern recognition in human cognition is therole of continuity of actions in time and space. While perception oftime appears to be continuous, it may actually be discrete, with a smalltime step. Conventional video technologies are all based on discretetime, but appear continuous. Further, memories are stored with timereferences, even if the reference clock may not be absolutelycalibrated. But what we remember is not raw sensor data, but rather thenarrative of the virtual reality at a given time. Key memories are thoseactions that were noticed at the time they were happening; thus, itmakes little sense to try to recall an aspect that was not noticed atthe time—that level of detail was not part of the initial memory. Insome cases, this level of filtering and abstraction of memories is notlimiting on a machine. For example, a machine may model the environment,and predict future outcomes for a series of actions, to some limit,e.g., 1 hour or five successive actions. However, as time passes, oradditional successive actions are performed, the error between theprediction and the actual outcome may be more accurately determined, andfactors or facts other than, or in addition to, those which were a partof the initial narrative may be included within the memories, whether ornot they were initially noticed or given significance at the time theywere happening. Of course, such retroactive supplementation of thedatabase, memories, or training consumes processing power, and thereforea balance must be maintained between ability to take the best currentaction based on the current memories, and seeking to improve futureperformance by reprocessing old data. Typically, the reprocessing taskmay be accomplished outside of the situational awareness in thereal-time response system, and therefore represents a supplement to thecore functionality of the agent. However, learning from mistakes andimprovement of predictive performance, i.e., seeking to model theenvironment in the virtual reality environment using the most importantfactors (similar to “principal components”), in a dynamic way is animportant feature for sustained usefulness.

Similarly, the memory structure of a conscious machine of the presentinvention would include memories of trajectories in time from thepresent and recent past, as shown in FIG. 7. They would also includeprojections to the future, which are then mapped onto the present, andmoved to the past. Memories from the more distant past may be stored ina similar way, although they may be stored in a different location. Allof these permit the re-enactment of past events, in a way similar to howthey were experienced at the time. Memories may be time-stamped by aclock, so that they may be retrieved by reference to the time, oralternatively by reference to content contained in the memory.

FIG. 7 suggests that sequential memories in time may be located inadjacent memory addresses, and are shifted to the past as triggered by aclock, similar to a shift register. However, in an alternativeembodiment, the memories may be static and not adjacent, but may belinked together as a linked list. In that case, a moving pointer mayrecord new memories at the front of the list.

Both the conscious module and the unconscious module may be implementedusing artificial neural networks, or simulations of such networks usingmore conventional computer hardware. Hardware components may includegraphical processing units (GPUs), tensor processing units (TPUs),field-programmable gate arrays (FPGAs), memristor arrays, or customneuromorphic chips. The neural networks may comprise a plurality ofhidden layers between input and output (see FIG. 2), in order thatcomplex models of the self, other agents, and objects may beaccommodated. The models may be trained using a real externalenvironment, or alternatively using a simulated environment, which maybe generated externally by a computer.

During and after training, the conscious module may be configured withan interface that permits the virtual reality environment to bemonitored externally, on either a continuous or sporadic basis. In thisway, an external user of a conscious machine may have additionalinformation on whether the machine is operating properly. Thisinformation may be useful in accelerating training of the machine.

In a preferred embodiment of the invention, the conscious machine may bepart of an autonomous or semi-autonomous vehicle. Such a vehicle couldtravel on roads, on the ground, on the surface of water, underwater, inthe air, or in space, or in some combination of these modalities. Itcould be used to transport one or more people or packages betweenlocations. Alternatively, it could record information about theenvironment that it passes through, and/or transmit such information toa remote location via a wireless (radio-frequency or optical) modality.

In an alternative preferred embodiment of the invention, the consciousmachine may travel in an information environment, for example in a cloudcomputing environment or the Internet. This could provide a moreefficient engine for searching a large distributed database, or forcontrolling a distributed sensor network, or for evaluating data frommany nodes of the Internet of Things.

In yet another preferred embodiment of the invention, the consciousmachine may comprise a personal digital assistant that may be carriedwith a person. In some cases, the machine may help to guide the personthrough the environment, or serve to aid the memory, or otherwiseanticipate the needs of the person. This may be particularly valuablefor individuals who may be handicapped or unable to navigate withoutassistance. The conscious module may provide more rapid evaluation ofincomplete data in environments that are unfamiliar to the individual.

In still another preferred embodiment, the conscious machine maycomprise an autonomous or semi-autonomous robot, which may be designedto operate in a variety of complex natural and artificial environments.Such a robot, with proper initial training and continued learning, mayexhibit a much greater flexibility for adaptation and autonomy inresponse to unpredictable events. Such a robot may be configured tointeract with humans in real time via either electronic or naturallanguage communication.

The conscious machine may be configured to communicate not only withhumans, but also with other similar or complementary conscious machines.In this way, one can envision a plurality of autonomous machines actingin concert to achieve a goal, without requiring direct real-time controlby humans.

The preferred embodiments presented here represent just a few examplesof designs and applications of conscious machines, and others may beenvisioned by those skilled in the art. It is therefore an object toprovide a conscious machine that is aware of its self, where thisconscious machine comprises the following components: a set of sensorinputs from an environment; an artificial neural network, receiving theset of sensor inputs, and being configured to identify the self, agents,and objects represented in the set of sensor inputs, recognizecorrelated patterns in time and space in the environment, and planachievement of a goal; and at least one automated processor, configuredto construct a simplified dynamical predictive model of the environmentthat includes the self, interacting with the agents and the objects; anda set of control outputs from the artificial neural network, dependenton the simplified dynamical predictive model, configured to alter theenvironment to implement the plan for achievement of the goal.

The repeated recognition of the self within the simplified dynamicalpredictive model of this conscious machine constitutes a primaryattribute of consciousness. The conscious machine may further comprise amemory, configured to store earlier representations of the simplifieddynamical predictive model, labeled by time and location in theenvironment, which may be retrieved either by reference to the time orthe associated content of the memory.

Furthermore, the neural network may be configured to learn fromexperience, subject to a set of predefined guidelines. The simplifieddynamical predictive model may represent a virtual reality thatsimulates the environment, whereby the model further comprises a causalnarrative centered around the self. The model may be configured toenable prediction of alternative futures, dependent on alternativeactions of the control outputs. The control outputs may be effective toalter a location monitored by the set of sensor inputs.

The sensor inputs may comprise one or more of visual images, auditorysignals, natural language, or electronic signals. The environment maycomprise a natural environment, an artificial environment, anenvironment representing an informational space, or some combination ofthese. The real-time state of the model may be monitored by an externaluser, which may be a human or alternatively another conscious machine.

The recognized self of the conscious machine may comprise an autonomousor semi-autonomous vehicle, or a robotic agent. The artificial neuralnetwork may evaluate the environment according to the effects on theself, and a portion of the evaluations may be represented as “emotions”incorporated into the model. The model may comprise an adaptive modelupdated to optimize a combination of fidelity to the environment,simplicity, and logical consistency.

The artificial neural network may comprise a network of interconnectedphysical electronic devices, each electronic device functioning as anartificial neuron. The network may comprise one or more of severalelectronic technologies, such as a GPU, a TPU, an FPGA, and aneuromorphic circuit. Alternatively, at least a portion of theartificial neural network may be simulated on a general-purpose computerprocessor having an instruction fetch unit, an instruction decode unit,and an arithmetic logic unit.

It is a further object to provide an intelligent machine control methodcomprising the following steps: configuring an artificial neural networkto accept sensor inputs from an external environment and to generatecontrol outputs that affect the environment; recording temporalsequences in the environment in a memory; training the artificial neuralnetwork to search for correlated temporal patterns in the sensor inputsfrom the external environment, in order to identify self, agents, andobjects in the environment; generating a simplified predictive dynamicalmodel of the environment, that permits predictions of future eventsinvolving the self, the agents, and the objects, dependent on thecontrol outputs; and updating the model to present a dynamic narrativeof the self in the environment.

Another object provides a self-aware system, comprising: a set of sensorinputs configured to receive sensor data representing a state of anenvironment; and at least one automated processor comprising anartificial neural network, receiving the set of sensor inputs,configured to: identify itself, other agents, and non-agent objectsrepresented in the set of sensor inputs; recognize correlated dynamictime and space patterns in the environment of itself, the other agents,and the non-agent objects; and construct a simplified dynamicalpredictive model of the environment that includes itself, interactingwith the other agents and the non-agent objects; and plan achievement ofa goal involving an interaction of itself with other agents and thenon-agent objects in the environment, to achieve a prospective change ina relationship of at least one of itself, other agents, and non-agentobjects in the environment.

Other objects will become apparent through a review of the descriptionprovided herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a classic von Neumann computer architecture of the priorart.

FIG. 2 shows a conceptual example of an artificial neural network of theprior art.

FIG. 3 shows a crude block diagram of a conscious mind and anunconscious mind interacting with the environment, which may be emulatedin a conscious machine.

FIG. 4 shows a preferred embodiment of a conscious computing module andan unconscious computer module, together with input and outputstructures.

FIG. 5 shows an agent moving in an environment and causing an object tomove.

FIG. 6 shows a conceptual diagram of a virtual reality environment withthe self, other agents, and objects.

FIG. 7 presents a preferred embodiment of a memory structure and timebase for a conscious machine.

FIG. 8 presents a flow chart for a preferred method for designing andtraining a conscious machine.

FIG. 9 shows alternative modes of programming artificial neural networks(ANNs) for conscious machines.

DETAILED DESCRIPTION OF THE INVENTION

Although consciousness has been difficult to define, most researchers inartificial intelligence would agree that AI systems to date have notexhibited anything resembling consciousness. Conventional views ofconsciousness are mostly illusory, so that a new definition ofconsciousness may provide a basis for developing a conscious machine.The key is pattern recognition of correlated events in time, leading tothe identification of a unified self-agent.

The sense of consciousness may represent the real-time activation ofneural circuits linking the present self with the past self. Such aconscious system can create a simplified virtual environment, edit it toreflect updated sensory information, and segment the environment intoself, other agents, and relevant objects. It can track recent timesequences of events, predict future events based on models and patternsin memory, explore possible results of future actions, extrapolate basedon past trends or experiences, and attribute causality to events andagents. It can make rapid decisions based on incomplete data, and candynamically learn new responses based on appropriate measures of successand failure. In this view, the primary function of consciousness is thegeneration of a dynamic narrative, a real-time story of a self-agentpursuing goals in a virtual reality.

A conscious machine of this type may be implemented using appropriateneural networks linked to episodic memories. Near-term applications mayinclude autonomous vehicles and flexible robotic assistants.

A major illusion of human consciousness is one of unified top-levelcontrol. FIG. 3 shows a diagram representing a conscious mind C above anunconscious mind U, which in turn is above the environment E. We believethat our conscious mind controls most of what we do, and that ourconscious mind is in direct communication and control of theenvironment. But psychological research has shown that in many cases,actions are triggered slightly before the conscious mind is aware ofthem, suggesting that these may actually be directed unconsciously. Theconscious mind may take credit for these actions, which form part of anarrative of conscious action. There are other internal experiences ofconsciousness, including perception of time and space, identification ofa unified self linked to past memories, emotions, and actions, andidentification of other agents and objects. A consistent explanation ofthe mind needs to account for all of these. Further, neither C nor U isfully rational, but U creates a consistent simplified narrative that Cexperiences. The present application proposes that similar aspects maybe emulated in a conscious machine, with a similar organization.

Many aspects responsible for human consciousness are hidden from view,and may not be evident either in the structure of the brain or in theinternal experience of consciousness. But as disclosed here,consciousness seems to involve a self-identified agent following acontinuous, causal narrative in a dynamic virtual environment. Theenvironment must integrate various sensory modalities with relevantemotions and memories. This is shown in FIG. 4, which showsconsciousness as a virtual reality (VR) construct created from filteredinput data, and representing a simplified dynamic model of the reality.The interaction with the external environment occurs via filtered inputsand outputs to the unconscious mind, where most of the detailedcoordination and decision takes place. The VR environment represents theself, acting in a simplified environment, comprised of objects and otheragents. Previous frames of the VR are saved in memory, and may beretrieved as needed.

A conscious visual representation of an object (such as a rose) is notjust a portion of a larger two-dimensional image. Rather, it is anobject embedded in a three-dimensional space, which represents theconcept of a rose and links to abstract attributes and memories ofimages and aromas (even if ancillary sensory stimulation, such as smell,is not currently present). This may be analogous to a PDF file of ascanned document which is subjected to optical character recognition(OCR). Such a file contains an image of a given page, but the image ofeach character is also associated with the functional representation ofthe character. Now imagine that the document also contains embeddedlinks to definitions of words and other relevant documents, and onestarts to have the richness necessary for consciousness. In contrast toa static environment of a document, the VR environment is highlydynamic, and is rapidly updated in time.

Another aspect of consciousness is the subjective sense of agency. Thisis created by activation of an adaptive neural net, primed to recognizeself-agency and causality, and also to recognize external agents. Thedynamic VR is built around such actors, as shown in FIG. 6. Note thatrecognition of agency is really a form of temporal pattern recognition,as suggested in FIG. 5, which shows an agent moving up to a fixedobject, which then starts to move. We live in a world that is continuousand causal, so that simplified causal models based on observed temporalcorrelations are generally highly functional. Furthermore, this is acase of dynamic learning; the mind learns to generate and refine asimplified model which maintains effective representation of theexternal environment.

A further aspect of consciousness is a sense of urgency or priorities.Often, the issues have different objective classifications, and theranking requires some normalization of different classes of issues. Acost or distance function may therefore be included within the model.Consistent with our understanding of consciousness, this ranking orranking function may be highly dynamic, and have what are apparentlyirrational characteristics, with biases, systematic “errors”, and other“personal” characteristics. While in some cases, such as an autonomousvehicle controller is desired to avoid unpredictable action orirrational behavior, in other cases, it is exactly these attributes thatmake the system better appear as being conscious.

In a further key aspect, this view of a conscious mind or a consciousmachine requires both a clock and a memory structure. Consider ashort-term dynamic memory module, containing the recent past, and apredictive model of one or more near futures, as shown in FIG. 7. Aclock time-stamps the present frame and shifts it to the past. Twoalternative futures may be presented, based on present actions. When oneaction is selected, this shifts that future into the present. Thisensures that perceived time is a central element in consciousness. Thesubjective sense of self is associated with the repeated activation ofself-recognition circuits, mapping the present self onto the past self.

Longer term memories are also stored, possibly in another location,based on a different technology. These may be retrieved either by timereference or by content. The memory elements may be VR frames, but otherparameters abstracted from one or more prior experiences may beindependently stored and retrieved.

The presence of a conscious VR module does not preclude a machine ormind from also incorporating unconscious perception and control vianeural circuits that do not interact with consciousness. For example,detailed coordination of specific muscles in walking or running is anessential part of brain activity, but is generally completelyinaccessible to the conscious mind. Similarly, a conscious machine willincorporate a hybrid approach, assigning to the conscious module onlythose aspects that cannot be achieved efficiently by a more conventionalmachine learning approach.

Indeed, we are accustomed to think of consciousness as a superior formof brain activity, but it might be better to think of a conscious mindas a specialized niche processor that should only be used as a lastresort. For example, consider a car driving along a road, with a largepuddle in the road. Is this a shallow puddle that one should simplydrive through, or a deep pothole that should be avoided? If the answeris obvious, this need not rise to the level of consciousness. But if itrequires a more complex assessment of weather conditions and the localstatus of road repair, the decision needs to be made at a higher level.There is a danger in this—such high level decisions based on incompletedata can be slow, at a time when a rapid decision is necessary. And thedecision can still turn out to be the wrong one. However, any systemthat significantly increases the likelihood of improved decision makinghas substantial value, for a self-driving car or other autonomoussystem. For example, a conscious system might further considerramifications of error (or success). If the puddle is shallow, and thevehicle swerves to avoid it, what are the risks of accident ordiscomfort for the passengers? If the puddle is deep and the tire hitsit, what damage could occur? If the car brakes suddenly, what is therisk of skidding or a rear-end accident? Etc.

As with other neural network systems, the initial design and training ofa conscious machine are essential, and a simplified flowchart of stepsin initializing a conscious module are presented in FIG. 8. The firststep is to design an initial model for the machine moving in itsenvironment. What are the important sensors and actuators needed, andwhat are the relevant timescales? Second, the neural network must beconfigured to evaluate sequences in time and space, and designed to usetemporal pattern recognition to identify agents in the environment. Themost important agent is the “self”, which is correlated with the sensorsand actuators. Other agents and objects can also be identified, and avirtual reality environment can be generated based on the interactionsof the self with these other agents and objects. This machine can betrained initially using a simplified external environment. In somecases, the initial training environment might be a computerized VRsystem. Finally, the machine can be transferred to the real world, wherelearning will continue. Throughout the training and learning, a methodto monitor the internal VR environment would be highly advantageous.This would be preferable to simply observing the functional behavior.One can imagine that training young children or animals would be mucheasier if we could really read their minds!

It is known in the prior art that deep neural networks may beimplemented in a variety of technologies, including digital and analogcircuits, biological neurons, CMOS transistors, superconductingJosephson junctions, memristors, phase-change memories, and resistiveRAMs, based on pulse or voltage-level logic. They may be implemented ina variety of circuit architectures, including not only conventionalprocessors, but also special-purpose processors such as GPUs, TPUs, andFPGAs. The deep neural networks comprising a conscious machine may inprinciple be implemented on any device technology and architecture thatmay be configured to support temporal pattern recognition, and toidentify agents of change in the virtual environment created by the setof sensory inputs.

This temporal pattern recognition represents a repeated processactivated by a periodic clock which establishes a time base. Thefrequency of this updating can be relatively slow if the systems dealswith slow changes in typical human environments; for comparison, thealpha rhythm in human brains is of order 10 Hz. But electronic systemsare capable of much faster operation, so that faster updating might beappropriate for application to a rapidly moving autonomous vehicle, forexample.

In order to be able to act with a sufficient level of sophistication ina complex environment, the neural network of a conscious machine mustcomprise a large number of neurons with an even larger number ofinterconnections. For example, the network must have at least millionsof neurons, with at least billions of interconnections. The strengths ofthe interconnections representing memories and associations mustpreferably be non-volatile over long periods of time, or at least fullybacked up in case of power failure. The memories must be capable ofrepeated adjustment and readout, with high reliability and very low rateof failure. The system should be able to be temporarily turned off, sothat repairs or upgrades may be implemented, and then turned back on ina way that remembers past performance.

The neural networks comprising a conscious machine must be capable oflearning during an initial supervised or unsupervised training period,but should also continue to learn during full operation in the field. Inthis way, a conscious machine comprising a robot or autonomous vehiclecould be optimized for operation in an environment with distinct butunpredicted characteristics.

Furthermore, in some cases, the initial training period may beaccelerated by pre-programming some of the interconnections, based on areadout of the internal interconnections of another conscious machinethat has already been trained. This effectively comprises implanting aset of artificial memories and experiences into a given machineconsciousness. It may be advantageous to use such a procedure to createa plurality of identically trained conscious machine twins or clones. Inthis way, a standard machine with reliable and validated performance maybe efficiently mass-produced. Further adaptation to custom environmentsmay be obtained in the field.

Biological neural networks are not normally designed to read out theirinternal connections, but if such a readout were available, it could inprinciple enable an artificial system to emulate aspects of the mind ofa biological organism. That could enable, for example, the creation ofan artificial pet that could better emulate some aspects of the behaviorof a biological pet.

These alternative modes of programming artificial neural networks (ANNs)for conscious machines are illustrated in the block diagram of FIG. 9.The memory weights in the interconnections of the ANN may be determinedby simulating a conscious machine in a virtual environment, or bytraining the machine in a natural environment, or even by reading outthe states of an analogous biological neural network (should that becomefeasible). These can then be written to the states of untrained ANNs ofa plurality of conscious machines. These machines can then bedistributed to customers, where further learning in a variety of naturalenvironments can continue.

Biological behavior in social animals may be governed in part by issuesof cooperation and competition. Cooperation depends in part on empathy,the ability to project the perspective of another agent on one's ownperspective, in order to better predict the action of other agents. Thismay be associated with “mirror neurons” in biological neural nets.Competition in social animals may depend on dominance hierarchies, whichprovide rules to negotiate differences in order to avoid conflict. In anenvironment which may comprise a plurality of both people and consciousmachines, it may be important that the machines incorporate mechanismsof both cooperation and competition. Similar issues have beenanticipated in the literature of science fiction, for example in theThree Rules of Robotics of Isaac Asimov(en.wikipedia.org/wiki/Three_Laws_of_Robotics).

Indeed, in an environment of interacting conscious machines, an exchangeof virtual reality environments between cooperative agents may occur, sothat one machine can better predict outcomes of interactions with othermachines, and improve its own virtual reality environment. Suchinter-machine communication could be acoustic or visual, but it couldalso occur via wireless rf communication channels. Of course, in somecases, agents are competitive, and this exchange would be disfavored.

One aspect of biological consciousness that is not generally consideredfor artificial intelligence is the role of sleep and dreams. But sleepand dreams are universal among animals, and their deprivation is highlydeleterious, suggesting that they must serve an important function, evenif it not well understood. Some recent research has suggested thatneural interconnections in animals grow dramatically during the day, butare selectively pruned back during sleep. This can be a form ofconsolidating learning, which may be important to emulate in machinesystems. Furthermore, dreams are associated with rapid eye movement(REM), which is also common among animals, suggesting that they, too,dream during sleep. Dreams are a form of virtual reality that issomewhat similar to the real-world experience. The difference, ofcourse, is that there is no sensory input during dreams, but there isstill a sense of self and a dynamic narrative. Dreams may also representpart of the learning process. A conscious machine could also operatewithout sensor input, but with some low-level activation of memories.Perhaps we will know that we have made conscious machines when we canobserve them dream.

A conscious machine might present another issue that is normallyrestricted to brains: mental illness. For example, malfunctioning of theVR generator may present distorted perceptions or narratives, whichmight be analogous to paranoia or schizophrenia. Furthermore, memoryactivation thresholds that are too high or too low might causehyperactivity, depression, or obsessive behavior. While this is purelyspeculative at present, it may be important to monitor a consciousmachine for abnormal behavior or thoughts. From another point of view, amalfunctioning conscious machine might even represent a model for humanmental behavior.

Finally, a reliable, inexpensive, conscious machine would enable a widerange of potential applications, many of which have not even beenconsidered. Going beyond autonomous vehicles or intelligent digitalassistants, one can imagine a variety of security and militaryapplications, from surveillance to monitoring the Internet to activedefense. Similarly, one can envision intelligent medical systems formonitoring and diagnosing patients, in the absence of medical personnel.Alternatively, one might have an artificial pet, a companion withoutsome of the requirements and shortcomings of dogs or cats. The onlylimit is our own imaginations.

In a medical environment, the conscious machine may seek to model thebehavior of a patient. As characteristics of the patient aresuccessfully modelled and their predictive nature verified, it may thenbe possible to analyze the virtual reality environment to determinedeviations from normal, and thus the system could be part of adiagnostic or therapeutic device.

More generally, the system may be used to implement user models,typically within a particular knowledge domain, to provide assistiveagents.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thescope of this present invention.

What is claimed is:
 1. A computational system configured to interactwith an environment, comprising: at least one input port configured toreceive information relating to a self agent, other agents, and objectsin an environment; at least one output port configured to produce anoutput to change a relationship between the computational system and theenvironment; a goal defining processor, configured to establish at leastone goal based on at least a set of rules for interacting between theself agent and the other agents and objects in the environment; asimplified representation processor, configured to periodically update asimplified representation of the environment which provides a coherentnarrative of the self agent interacting with the other agents and theobjects in the environment, based on at least the received information,and store a series of the simplified representations in a memory; apredictive causal model, configured to predict a plurality ofalternative future states of the self agent, the other agents, and theobjects within the environment; and a decision processor, configured todecide between prospective alternate actions of the self agent, based onat least the at least one goal, the stored simplified representations ofthe environment, and the plurality of alternative future states.
 2. Thecomputational system according to claim 1, wherein the output isdependent on the decision between the prospective alternate actions. 3.The computational system according to claim 1, wherein the output isdependent on the simplified representation processor and independent ofthe decision processor.
 4. The computational system according to claim1, further comprising a dynamic temporal pattern recognition processor,configured to process the received environmental information andrecognize changes in the self agent, the other agents, and the objectsin the environment, wherein the predictive causal model is responsive tothe recognized change.
 5. The computational system according to claim 1,further comprising a feedback processor, configured to refine thepredictive causal model using success in meeting the at least one goalrepresented in the received information as a basis for feedback.
 6. Thecomputational system according to claim 1, further comprising areference clock configured to define a period for the periodic updatesof the representation.
 7. The computational system according to claim 6,further comprising a retrieval processor configured to retrieverespective simplified representations of the environment by time-stampand by a content of the respective simplified representations of theenvironment.
 8. The computational system according to claim 1, whereinthe environment comprises a real physical environment and the self agentfurther comprises a navigational process for navigating in the realphysical environment.
 9. The computational system according to claim 8,where the self agent comprises at least one of an autonomous vehicle anda robot.
 10. The computational system according to claim 1, wherein theother agents are autonomous navigation mobile devices.
 11. Thecomputational system according to claim 1, wherein the environmentcomprises a virtual environment in which the self agent can navigate,for training of the computational system.
 12. The computational systemaccording to claim 1, further comprising a communication processconfigured to communicate with the other agents in the environmentcomprising human agents and computational agents.
 13. The computationalsystem according to claim 1, wherein the decision processor comprises anartificial neural network having a plurality of layers comprising atleast one hidden layer.
 14. The computational system according to claim13, wherein the artificial neural network comprises an automatedprocessor selected from the group consisting of at least one of a tensorprocessing unit, a graphics processing unit, a field programmable gatearray, and a neuromorphic chip.
 15. A computational method forinteracting with an environment, comprising: receiving informationrelating to a self agent, other agents, and objects in an environment;establishing at least one goal based on at least a set of rules forinteracting between the self agent and the other agents and objects inthe environment; periodically updating a simplified representation ofthe environment which provides a coherent narrative of the self agentinteracting with the other agents and the objects in the environment,based on at least the received information, and store a series of thesimplified representations with at least one automated processor;predicting a plurality of alternative future states of the self agent,the other agents, and the objects within the environment with apredictive causal model implemented on the at least one automatedprocessor; and deciding between prospective alternate actions of theself agent with the at least one automated processor, based on at leastthe at least one goal, the stored simplified representations of theenvironment, and the plurality of alternative future states; produce anoutput to change a relationship between the computational system and theenvironment.
 16. The computational method according to claim 15, whereinthe output is dependent on the decision between the prospectivealternate actions.
 17. The computational method according to claim 15,wherein the output is dependent on the simplified representationprocessor and independent of the decision.
 18. The computational methodaccording to claim 15, further comprising processing the receivedenvironmental information to recognize changes in the self agent, theother agents, and the objects in the environment, wherein the predictivecausal model is responsive to the recognized change.
 19. Thecomputational method according to claim 15, further comprising refiningthe predictive causal model using success in meeting the at least onegoal represented in the received information as a basis for feedback.20. A method for controlling interactions of a respective agent withinan environment, comprising: defining a set of rules for interactingbetween the respective agent and the environment; recognizing activeother agents in the environment, objects in the environment, and therespective agent in the environment; recognizing changes in theenvironment with a dynamic temporal pattern recognition system based onan environmental status input; sequentially generating and storing aseries of simplified representations of the environment comprising otheragents, the respective agent, and the objects; defining at least onegoal for the respective agent based on at least the set of rules; makingalternative predictions of future states of the other agents, therespective agent, and objects within the environment using a predictivecausal model, based on alternate different actions by the respectiveagent and past representations stored in the memory; generatingdecisions based on the goals and the alternate predictions; and refiningthe predictive causal model using success in meeting the goals as abasis for feedback.